An AI-powered performance tool that analyzes component usage to identify and suggest candidates for lazy loading.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install lazy-load-gen
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install lazy-load-gen using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Lazy Load Suggester?
Lazy Load Suggester is a specialized utility designed to streamline frontend performance optimization by automating the identification of code-splitting opportunities. By analyzing your project's component hierarchy and import relationships, it pinpoints heavy modules that are unnecessarily included in the initial bundle. This tool is a vital part of the Openclaw Skills library, helping developers reduce the critical path size and improve the overall user experience through intelligent architectural suggestions.
Lazy Load Suggester Use Cases
- Reducing the initial bundle size of large-scale web applications.
- Improving Time to Interactive (TTI) scores for performance-sensitive projects.
- Implementing modern code-splitting strategies in legacy codebases.
- Identifying non-essential components like modals, charts, or editors that load below the fold.
How Lazy Load Suggester Works
- The tool scans your source code directory to map out component imports and estimate component weight.
- It evaluates which components are loaded during the first paint versus those that could be deferred.
- The AI processes the dependency graph to find high-impact lazy loading candidates.
- It generates a detailed report with implementation examples using dynamic imports or framework-specific lazy loading syntax.
Lazy Load Suggester Setup
Lazy Load Suggester requires Node.js 18 or higher and an OpenAI API key. No permanent installation is necessary as it runs via npx.
export OPENAI_API_KEY='your_api_key'
npx ai-lazy-load ./src/
Lazy Load Suggester Data Schema & Taxonomy
The tool analyzes local file structures and outputs actionable recommendations. The analysis focuses on the following metadata:
| Metadata Field | Description |
|---|---|
| Component Path | The file location of the identified component candidate. |
| Impact Score | The relative importance of lazy loading the component based on size. |
| Usage Pattern | How and where the component is imported across the application. |
| Code Snippet | Suggested React.lazy or dynamic import implementation code. |
Lazy Load Suggester Advanced Features
- Targeted directory scanning to focus on specific modules or page routes.
- Zero-configuration setup for rapid performance auditing during development.
- AI-driven context awareness that understands the difference between critical and non-critical UI elements.
- Seamless workflow integration as part of the broader collection of Openclaw Skills.
SKILL.md
Loading
Related Openclaw Skills
Landing Page Generator
A CLI utility that transforms your project's package.json into a fully responsive, AI-generated HTML landing page with a single command.
Kubernetes Manifest Generator (k8s-gen)
An AI-driven CLI tool that transforms Docker Compose files and plain text descriptions into production-ready Kubernetes YAML manifests.
K8s Generator
A developer tool for generating production-ready Kubernetes manifests from docker-compose files or plain English descriptions.
JWT Decode - Token Inspector CLI
A command-line utility for decoding, inspecting, and validating JSON Web Tokens without leaving your terminal.
Lazy Load Suggester
An AI-powered analysis tool that identifies heavy components for lazy loading to drastically reduce initial bundle sizes.
Lazy Load Analyzer
An AI-driven CLI tool that analyzes component trees to identify lazy loading opportunities and optimize initial page load performance.
Featured*
